Adaptive optimization of large-margin learning for physics-informed time–frequency maps for the diagnosis of rotating components

This article introduces a novel physics-informed large-margin learning framework for diagnosing rotating component faults, addressing the limitations of traditional methods and “black-box” artificial intelligence models in handling complex, noisy vibration data. The methodology integrates feature mode decomposition and short-time Fourier transform to generate physics-informed time–frequency maps, from which interpretable features are extracted. A large-margin learning model then utilizes these features, with its five critical hyperparameters adaptively optimized using the slime mould algorithm to ensure robustness and optimal performance. Evaluated on the Case Western Reserve University (CWRU) and intelligent maintenance systems (IMS) bearing datasets, the proposed framework achieves a diagnostic accuracy of 88.75% on CWRU and 96.67% on IMS, outperforming seven state-of-the-art classification baselines implemented under identical experimental conditions. This approach enhances interpretability, diagnostic accuracy, and generalization, offering a more reliable solution for machine health monitoring.